INSIGHTS INTO MINIMAX APPROVAL VOTING: NAVIGATING APPROXIMATION AND PARAMETERIZED COMPLEXITY LANDSCAPES (International journal of artificial intelligence, 2024)
This study delves into the intricate realm of Minimax Approval Voting, shedding light on its characteristics from both approximation and parameterized complexity perspectives. By navigating through these diverse landscapes, we offer insights into the challenges and opportunities inherent in optimizing Minimax Approval Voting scenarios. The exploration of approximation algorithms provides a nuanced understanding of the trade-offs between computational efficiency and solution quality, while the investigation into parameterized complexity unveils the tractability and inherent complexities of the voting model. Our findings contribute to the theoretical foundations of Minimax Approval Voting, offering valuable guidance for practitioners and researchers engaged in the design and analysis of voting systems.